The idea of fencing between infected and healthy communities, termed cordon sanitaire, has been deployed during a variety of outbreaks for centuries. In line with this principle, as an exit strategy, many countries have transitioned to a system of “zonal (or local) lockdown” [16]. This system entails identification of specific “hotspots” where a sudden outbreak cluster, with a high number of cases, have been identified in real time. Such clustered social distancing works by dividing the population into “zones” according to the geospatial distribution of disease cluster contained within, so that interactions within a zone are significantly greater than interactions between zones [17]. Transmission hotspots, or “red zones” are subject to strict lockdown measures than “green zones”, where very few or no new cases have been identified for several days. Such strategies were adopted in France [17], with green zones defined by areas where the virus transmission is relatively low and there is not as burdensome pressure placed on the healthcare system.
The “zonal lockdown” approach has several important requirements. First, this categorisation of hotspots is typically a dynamic process, which requires an ability to reliably identify, in real time, areas that meet or fall short of the pre-specified lockdown criteria. This requires continuous data-driven feedbacks on: (1) regional daily confirmed cases (either by date of reporting or onset of symptoms), and (2) other time-series information needed to calculate the changes in region-specific effective reproduction number (R, the average number of secondary infections per infected individual), including daily numbers of hospitalized cases, daily numbers of deaths in different age groups, and transmission dynamics (eg, average time from infection to death) [17]. While such strategy has been successfully established in developed settings (such as France, where testing is widespread with 0.52 daily tests being done per 1000 population), this remains challenging in many LMICs due to (1) absence of large-scale population surveillance system based on randomly-selected individuals (e.g., in Bangladesh, the testing approach has focused on purposive, self-referred samples, with significant selection bias), and (2) poor testing laboratory facilities and reporting capacities (e.g., in Pakistan, only 0.09 daily tests are being conducted per 1000 individuals) [18]. In this regard, India has adapted a scalable mass "Pool testing" approach [19]. This cost-effective strategy involves collecting multiple samples in a tube and testing them with a single RT-PCR assay run. If the test is negative, all the people tested are negative. If it is positive, every person has to be tested individually for the virus. This approach reduces the time needed to test large swathes of the population [20].
Second, the classification of the zones should also be multifactorial. This should not only take into consideration the incidence rate, but also the other epidemiological (e.g., doubling rate of new cases; number of deaths) and administrative aspects (e.g., available hospital and ICU beds; testing and surveillance structure; residential versus industrial zone). Third, managing the zones efficiently to reduce transmission both within and outside of the zones is a major undertaking. Recent reports from India shows that infection size in many containment areas is 100-fold to 200-fold higher than the cases reported at those sites—indicating that containment efforts within zones may not have fully paid off [21]. Therefore, detailed apriori standard operating procedures should be devised to include aspects on (1) within-zone public health measures (eg, risk communication, house-to-house surveillance, test booths, contact-tracing, case referral systems, ambulance and medical facilities), (2) within-zone measures of emergency services (eg, food supply, law enforcement, isolation centres, and burial facilities), and (3) outside-zone measures such as creation of “buffer” zones (e.g., in India [19]) that surround the main containment zone to minimise out-of-zone transmissions. Such detailed protocols are crucial for efficiency. In Iran, for example, suboptimal zone management has increased risk of a second wave [22]. Finally, similar to sustained mitigation strategy, the zonal lockdown will be most effective when the overall rate of infection is in decline, accompanied by exhaustive vigilance.
While zonal lockdown, if implemented properly, can help contain the spread of the virus, efficacy of this approach can be reduced by other concurrent transmission networks, such as those linked to economic and social interdependency between zones [17]. Additionally, the impacts on the economy, particularly inside the zones, can be considerably more severe than under mitigation where the economy essentially opens with restrictions, exacerbating economic hardship in countries with already weak economic performance and social security nets. Therefore, these aspects merit careful consideration during the planning phase of this strategy.